以Nakagami m-參數分析超音波基頻與組織諧波之肝影像

肝硬化（hepatic cirrhosis）是各種慢性肝病發展的晚期階段。病理上以肝臟彌漫性纖維化、再生結節和假小葉形成為特徵。對於肝臟疾病，超音波檢查往往是首先被用來做影像學診斷的，因為它很簡單，價格低廉，和無侵入性。目前在醫院的超音波部門，組織諧波被廣泛的應用在肝疾病上。相較於傳統基頻超音波，組織諧波對於區域性的肝疾病，像是肝腫瘤、肝癌等，會有明顯的幫助。但對於瀰漫性肝疾病方面，像是肝纖維化、脂肪肝以及肝硬化，相較於傳統基頻超音波，組織諧波影像對於診斷上卻無法證實是否有明顯的幫助。本論文針對瀰漫性肝疾病中的肝硬化，利用Nakagami統計模型，以定量的方式來偵測肝硬化與正常肝兩者的診斷差別，並藉由合併傳統基頻超音波與組織諧波訊號計算Nakagami m-value，以找出最佳幫助診斷的方式。

關鍵字

組織諧波；肝硬化； Nakagami m-value

並列摘要

The hepatic cirrhosis is the late stage of a variety of chronic liver diseases. Pathologically, it features diffuse liver fibrosis, regenerative nodules, and formation of pseudo lobules. For liver diseases, ultrasound imaging is often the first means to be used for imaging diagnosis, because it is simple, inexpensive, and non-invasive. Currently in the ultrasound department of hospitals, tissue harmonic imaging is widely applied on diagnosis of liver diseases. The tissue harmonic imaging outperforms the traditional fundamental imaging for regional liver diseases, such as liver tumor and liver cancer. But for diffuse liver diseases, such as liver fibrosis, fatty liver, and hepatic cirrhosis, it is not yet proven whether the tissue harmonic imaging can provide obvious diagnostic helps in contrast to the traditional fundamental imaging. . This thesis focuses on detection of the diffuse liver cirrhosis by employing the Nakagami statistical model to differentiate livers with hepatic cirrhosis from normal livers in a quantitative way., By merging the fundamental and harmonic tissue images to calculate the combined Nakagami m-values, detection rates are significantly improved which has the potential to help clinical diagnosis.

並列關鍵字

tissue harmonic image ； cirrhosis of the liver ； Nakagami m-value

參考文獻

[1] Nicolas D, Nassiri DK, Gaarbutt P, Hill CR. Tissue characterization from ultrasound B-scan data. Ultrasound Med Biol 12:135–143, 1986

[2] Wu C, Chen Y, Hsieh K. Texture features for classification of ultrasonic liver images. IEEE Trans Med Imaging 11:141–152,1992

[3] Kadah MY, Farag AA, Zurada MJ, et al. Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images. IEEE Trans Med Imaging 15:466–478, 1996

[4] Mojsilovic A, Popovic M, Sevic D. Classification of the ultrasound liver images with the 2 x 1-D wavelet transform. IEEE Int Conf Image Processing 1:367–370, 1996

[5] Mojsilovic A, Popovic M, Markovic S, Krstic M. Characterization of visually diffuse diseases from B-scan liver images using non-separable wavelet transform. IEEE Trans Med Imaging 17:541–549, 1998

國際替代計量

以Nakagami m-參數分析超音波基頻與組織諧波之肝影像

主題瀏覽